SPG: Structure-Private Graph Database via SqueezePIR
Summary: SPG provides a structure-private graph database for GNN workloads by hiding access-pattern leakage (which node/neighbor is accessed) via PIR. Introduces SqueezePIR, a compression-optimized PIR yielding ~11.85× speedup vs FastPIR with <2% accuracy loss. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Ling Liang
- 2. Jilan Lin
- 3. Zheng Qu
- 4. Ishtiyaque Ahmad
- 5. Fengbin Tu
- 6. Trinabh Gupta
- 7. Yufei Ding
- 8. Yuan Xie
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,545 | OpenFGL: A Comprehensive Benchmark for Federated Graph Learning | 2025 | VLDB | 4.1945683e-05 |
| 10,672 | Sectric: Towards Accurate, Privacy-preserving and Efficient Triangle Counting | 2025 | VLDB | 4.1945683e-05 |
| 11,270 | Private Information Retrieval in Large Scale Public Data Repositories | 2023 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 278 | AliGraph: A Comprehensive Graph Neural Network Platform | 2019 | VLDB | 0.00029230623 |
| 4,940 | Privacy Preserving Subgraph Matching on Large Graphs in Cloud | 2016 | SIGMOD | 5.8180285e-05 |
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